Dear list, I am interested in fitting a Generalized Additive Mixed Model with spatially correlated errors to a large, spatially indexed, data set (~4000 observations).
My initial analysis was a Generalized Additive Model that included a two dimensional smooth term to model spatially correlated effect (i.e. s(latitude,longitude)). The problem is that the residuals of this model are still spatially correlated, so it seems that I should use a GAMM in which the spatial autocorrelation is modeled explicitly. The problem is that, as stated in the documentation of the mgcv package, my dataset is too large for the gamm function. Is anybody aware of an alternative approach to analyze this data? Julian M. Burgos Fisheries Acoustics Research Lab School of Aquatic and Fishery Science University of Washington 1122 NE Boat Street Seattle, WA 98105 Phone: 206-221-6864 ______________________________________________ [email protected] mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
